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Double-Target Based Neural Networks in Predicting Energy Consumption in Residential Buildings

Author

Listed:
  • Hossein Moayedi

    (Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
    Faculty of Civil Engineering, Duy Tan University, Da Nang 550000, Vietnam)

  • Amir Mosavi

    (Faculty of Civil Engineering, Technische Universität Dresden, 01069 Dresden, Germany
    School of Economics and Business, Norwegian University of Life Sciences, 1430 Ås, Norway
    John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary
    School of the Built Environment, Oxford Brookes University, Oxford OX3 0BP, UK)

Abstract

A reliable prediction of sustainable energy consumption is key for designing environmentally friendly buildings. In this study, three novel hybrid intelligent methods, namely the grasshopper optimization algorithm (GOA), wind-driven optimization (WDO), and biogeography-based optimization (BBO), are employed to optimize the multitarget prediction of heating loads (HLs) and cooling loads (CLs) in the heating, ventilation and air conditioning (HVAC) systems. Concerning the optimization of the applied algorithms, a series of swarm-based iterations are performed, and the best structure is proposed for each model. The GOA, WDO, and BBO algorithms are mixed with a class of feedforward artificial neural networks (ANNs), which is called a multi-layer perceptron (MLP) to predict the HL and CL. According to the sensitivity analysis, the WDO with swarm size = 500 proposes the most-fitted ANN. The proposed WDO-ANN provided an accurate prediction in terms of heating load (training (R 2 correlation = 0.977 and RMSE error = 0.183) and testing (R 2 correlation = 0.973 and RMSE error = 0.190)) and yielded the best-fitted prediction in terms of cooling load (training (R 2 correlation = 0.99 and RMSE error = 0.147) and testing (R 2 correlation = 0.99 and RMSE error = 0.148)).

Suggested Citation

  • Hossein Moayedi & Amir Mosavi, 2021. "Double-Target Based Neural Networks in Predicting Energy Consumption in Residential Buildings," Energies, MDPI, vol. 14(5), pages 1-25, March.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:5:p:1331-:d:508209
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